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Abstract Transcription-coupled repair (TCR) and global genomic repair (GGR) are two subpathways of nucleotide excision repair (NER). The TFIIH subunit Tfb1 contains a Pleckstrin homology domain (PHD), which was shown to interact with one PHD-binding segment (PB) of Rad4 and two PHD-binding segments (PB1 and PB2) of Rad2 in vitro. Whether and how the different Rad2 and Rad4 PBs interact with the same Tfb1 PHD, and whether and how they affect TCR and GGR within the cell remain mysterious. We found that Rad4 PB constitutively interacts with Tfb1 PHD, and the two proteins may function within one module for damage recognition in TCR and GGR. Rad2 PB1 protects Tfb1 from degradation and interacts with Tfb1 PHD at a basal level, presumably within transcription preinitiation complexes when NER is inactive. During a late step of NER, the interaction between Rad2 PB1 and Tfb1 PHD augments, enabling efficient TCR and GGR. Rather than interacting with Tfb1 PHD, Rad2 PB2 constrains the basal interaction between Rad2 PB1 and Tfb1 PHD, thereby weakening the protection of Tfb1 from degradation and enabling rapid augmentation of their interactions within TCR and GGR complexes. Our results shed new light on NER mechanisms. 

Advances in neural fields are enablling high-fidelity capture of shape and appearance of dynamic 3D scenes. However, this capbabilities lag behind those offered by conventional representations such as 2D videos because of algorithmic challenges and the lack of large-scale multi-view real-world datasets. We address the dataset limitations with DiVa-360, a real-world 360° dynamic visual dataset that contains synchronized high-resolution and long-duration multi-view video sequences of table-scale scenes captured using a customized low-cost system with 53 cameras. It contains 21 object-centric sequences categorized by different motion types, 25 intricate hand-object interaction sequences, and 8 long-duration sequences for a total of 17.4M frames. In addition, we provide foreground-background segmentation masks, synchronized audio, and text descriptions. We benchmark the state-of-the-art dynamic neural field methods on DiVa-360 and provide insights about existing methods and future challenges on long-duration neural field capture. 

Abstract We study a 1-dimensional discrete signal denoising problem that consists of minimizing a sum of separable convex fidelity terms and convex regularization terms, the latter penalize the differences of adjacent signal values. This problem generalizes the total variation regularization problem. We provide here a unified approach to solve the problem for general convex fidelity and regularization functions that is based on the Karush–Kuhn–Tucker optimality conditions. This approach is shown here to lead to a fast algorithm for the problem with general convex fidelity and regularization functions, and a faster algorithm if, in addition, the fidelity functions are differentiable and the regularization functions are strictly convex. Both algorithms achieve the best theoretical worst case complexity over existing algorithms for the classes of objective functions studied here. Also in practice, our C++ implementation of the method is considerably faster than popular C++ nonlinear optimization solvers for the problem. 

The past decade has witnessed tremendous advances in synthesis of metal halide perovskites and their use for a rich variety of optoelectronics applications. Metal halide perovskite has the general formula ABX 3 , where A is a monovalent cation (which can be either organic ( e.g. , CH 3 NH 3 + (MA), CH(NH 2 ) 2 + (FA)) or inorganic ( e.g. , Cs + )), B is a divalent metal cation (usually Pb 2+ ), and X is a halogen anion (Cl − , Br − , I − ). Particularly, the photoluminescence (PL) properties of metal halide perovskites have garnered much attention due to the recent rapid development of perovskite nanocrystals. The introduction of capping ligands enables the synthesis of colloidal perovskite nanocrystals which offer new insight into dimension-dependent physical properties compared to their bulk counterparts. It is notable that doping and ion substitution represent effective strategies for tailoring the optoelectronic properties ( e.g. , absorption band gap, PL emission, and quantum yield (QY)) and stabilities of perovskite nanocrystals. The doping and ion substitution processes can be performed during or after the synthesis of colloidal nanocrystals by incorporating new A′, B′, or X′ site ions into the A, B, or X sites of ABX 3 perovskites. Interestingly, both isovalent and heterovalent doping and ion substitution can be conducted on colloidal perovskite nanocrystals. In this review, the general background of perovskite nanocrystals synthesis is first introduced. The effects of A-site, B-site, and X-site ionic doping and substitution on the optoelectronic properties and stabilities of colloidal metal halide perovskite nanocrystals are then detailed. Finally, possible applications and future research directions of doped and ion-substituted colloidal perovskite nanocrystals are also discussed.